Dear Colleagues,
There are a number of significant steps in the development of machine learning that benefit from information theoretic analysis, as well as the insights into information processing that it brings. While we expect information theory to play an even more significant role in the next wave of growth in machine learning and artificial intelligence, we also recognize the new challenges in this task. There are indeed a set of lofty goals, where we hope to have a holistic view of data processing, to work with high-dimensional data and inaccurate statistical models, to incorporate domain knowledge, to provide performance guarantees, robustness, security, and fairness, to reduce the use of computational resources, to generate reusable and interpretable learning results, etc. Correspondingly, in theoretical studies, we shall need new formulations, new mathematical tools, new analysis techniques, and maybe even new metrics to evaluate the guidance and insights offered by theoretical studies.
The goal of this Special Issue is to collect new results in using information theoretic thinking to solve machine learning problems. We are also interested in papers presenting new methods and new concepts, even if some of these ideas might not have been fully developed, or might not have the most compelling set of supporting experimental results.
Some of the topics of interest are listed below:
- Understanding gradient descent and general iterative algorithms;
- Sample complexity and generalization errors;
- Utilizing knowledge of data structure in learning;
- Distributed learning, communication-aware learning algorithms;
- Transfer learning;
- Multimodal learning and information fusion;
- Information theoretic approaches in active and reinforcement learning;
- Representation learning and its information theoretic interpretation;
- Method and theory for model compression.
Deadline for manuscript submissions:Â 31 December 2021.Ìý
More details can be found atÂ
Guest editors:Â Dr. Lizhong Zheng and Dr. Chao Tian